18 Nov 2016
Research article |
| 18 Nov 2016
Performance of site-specific parameterizations of longwave radiation
Giuseppe Formetta, Marialaura Bancheri, Olaf David, and Riccardo Rigon
In this work 10 algorithms for estimating downwelling longwave atmospheric radiation (L↓) and 1 for upwelling longwave radiation (L↑) are integrated into the JGrass-NewAge modelling system. The algorithms are tested against energy flux measurements available for 24 sites in North America to assess their reliability. These new JGrass-NewAge model components are used (i) to evaluate the performances of simplified models (SMs) of L↓, as presented in literature formulations, and (ii) to determine by automatic calibration the site-specific parameter sets for L↓ in SMs. For locations where calibration is not possible because of a lack of measured data, we perform a multiple regression using on-site variables, i.e. mean annual air temperature, relative humidity, precipitation, and altitude. The regressions are verified through a leave-one-out cross validation, which also gathers information about the possible errors of estimation. Most of the SMs, when executed with parameters derived from the multiple regressions, give enhanced performances compared to the corresponding literature formulation. A sensitivity analysis is carried out for each SM to understand how small variations of a given parameter influence SM performance. Regarding the L↓ simulations, the Brunt (1932) and Idso (1981) SMs, in their literature formulations, provide the best performances in many of the sites. The site-specific parameter calibration improves SM performances compared to their literature formulations. Specifically, the root mean square error (RMSE) is almost halved and the Kling–Gupta efficiency is improved at all sites. Also in this case, Brunt (1932) and Idso (1981) SMs provided the best performances.
The L↑ SM is tested by using three different temperatures (surface soil temperature, air temperature at 2 m elevation, and soil temperature at 4 cm depth) and model performances are then assessed. Results show that the best performances are achieved using the surface soil temperature and the air temperature.
Received: 12 May 2016 – Discussion started: 31 May 2016 – Revised: 31 Oct 2016 – Accepted: 01 Nov 2016 – Published: 18 Nov 2016